In [ ]:
import ipywidgets as widgets
import sys
from pathlib import Path
import os
import importlib


module_path='preprocessing/day_intervals_preproc'
if module_path not in sys.path:
    sys.path.append(module_path)

module_path='utils'
if module_path not in sys.path:
    sys.path.append(module_path)
    
module_path='preprocessing/hosp_module_preproc'
if module_path not in sys.path:
    sys.path.append(module_path)
    
module_path='model'
if module_path not in sys.path:
    sys.path.append(module_path)
#print(sys.path)
root_dir = os.getcwd()
import day_intervals_cohort
from day_intervals_cohort import *

import day_intervals_cohort_v2
from day_intervals_cohort_v2 import *

import data_generation_icu

import data_generation
import evaluation

import feature_selection_hosp
from feature_selection_hosp import *

# import train
# from train import *


import ml_models
from ml_models import *

import dl_train
from dl_train import *

import tokenization
from tokenization import *


import behrt_train
from behrt_train import *

import feature_selection_icu
from feature_selection_icu import *
import fairness
import callibrate_output
In [ ]:
importlib.reload(day_intervals_cohort)
import day_intervals_cohort
from day_intervals_cohort import *

importlib.reload(day_intervals_cohort_v2)
import day_intervals_cohort_v2
from day_intervals_cohort_v2 import *

importlib.reload(data_generation_icu)
import data_generation_icu
importlib.reload(data_generation)
import data_generation

importlib.reload(feature_selection_hosp)
import feature_selection_hosp
from feature_selection_hosp import *

importlib.reload(feature_selection_icu)
import feature_selection_icu
from feature_selection_icu import *

importlib.reload(tokenization)
import tokenization
from tokenization import *

importlib.reload(ml_models)
import ml_models
from ml_models import *

importlib.reload(dl_train)
import dl_train
from dl_train import *

importlib.reload(behrt_train)
import behrt_train
from behrt_train import *

importlib.reload(fairness)
import fairness

importlib.reload(callibrate_output)
import callibrate_output

importlib.reload(evaluation)
import evaluation

Welcome to your MIMIC-IV Project¶

This repository explains the steps to download and clean MIMIC-IV dataset for analysis. The repository is compatible with MIMIC-IV v1.0 and MIMIC-IV v2.0

Please go to:

  • https://physionet.org/content/mimiciv/1.0/ for v1.0
  • https://physionet.org/content/mimiciv/2.0/ for v2.0

Follow instructions to get access to MIMIC-IV dataset.

Download the files using your terminal:

  • wget -r -N -c -np --user mehakg --ask-password https://physionet.org/files/mimiciv/1.0/ or
  • wget -r -N -c -np --user mehakg --ask-password https://physionet.org/files/mimiciv/2.0/

Save downloaded files in the parent directory of this github repo.

The structure should look like below for v1.0-

  • mimiciv/1.0/core
  • mimiciv/1.0/hosp
  • mimiciv/1.0/icu

The structure should look like below for v2.0-

  • mimiciv/2.0/hosp
  • mimiciv/2.0/icu

1. DATA EXTRACTION¶

Please run below cell to select option for cohort selection. The cohort will be svaed in ./data/cohort/

In [ ]:
print("Please select the approriate version of MIMIC-IV for which you have downloaded data ?")
version = widgets.RadioButtons(options=['Version 1','Version 2'],value='Version 2')
display(version)

print("Please select what prediction task you want to perform ?")
radio_input4 = widgets.RadioButtons(options=['Mortality','Length of Stay','Readmission','Phenotype'],value='Mortality')
display(radio_input4)

Please select the approriate version of MIMIC-IV for which you have downloaded data ?

RadioButtons(index=1, options=('Version 1', 'Version 2'), value='Version 2')
Please select what prediction task you want to perform ?

RadioButtons(options=('Mortality', 'Length of Stay', 'Readmission', 'Phenotype'), value='Mortality')

Refining Cohort and Prediction Task Definition¶

Based on your current selection following block will provide option to further refine prediction task and cohort associated with it:

  • First you will refine the prediction task choosing from following options -

    • length of Stay - You can select from two predefined options or enter custom number of days to predict length os stay greater than number of days.

    • Readmission - You can select from two predefined options or enter custom number of days to predict readmission after "number of days" after previous admission.

    • Phenotype Prediction - You can select from four major chronic diseases to predict its future outcome

      • Heart failure
      • CAD (Coronary Artery Disease)
      • CKD (Chronic Kidney Disease)
      • COPD (Chronic obstructive pulmonary disease)

  • Second, you will choode whether to perfom above task using ICU or non-ICU admissions data

  • Third, you can refine the refine the cohort selection for any of the above choosen prediction tasks by including the admission samples admitted with particular chronic disease -

    • Heart failure
    • CAD (Coronary Artery Disease)
    • CKD (Chronic Kidney Disease)
    • COPD (Chronic obstructive pulmonary disease)

print("Please run below cell to extract the cohort for selected options")

In [ ]:
if radio_input4.value=='Length of Stay':
    radio_input2 = widgets.RadioButtons(options=['Length of Stay ge 3','Length of Stay ge 7','Custom'],value='Length of Stay ge 3')
    display(radio_input2)
    text1=widgets.IntSlider(
    value=3,
    min=1,
    max=10,
    step=1,
    disabled=False,
    continuous_update=False,
    orientation='horizontal',
    readout=True,
    readout_format='d'
)
    display(widgets.HBox([widgets.Label('Length of stay ge (in days)',layout={'width': '180px'}), text1]))
elif radio_input4.value=='Readmission':
    radio_input2 = widgets.RadioButtons(options=['30 Day Readmission','60 Day Readmission','90 Day Readmission','120 Day Readmission','Custom'],value='30 Day Readmission')
    display(radio_input2)
    text1=widgets.IntSlider(
    value=30,
    min=10,
    max=150,
    step=10,
    disabled=False
    )
    display(widgets.HBox([widgets.Label('Readmission after (in days)',layout={'width': '180px'}), text1]))
elif radio_input4.value=='Phenotype':
    radio_input2 = widgets.RadioButtons(options=['Heart Failure in 30 days','CAD in 30 days','CKD in 30 days','COPD in 30 days'],value='Heart Failure in 30 days')
    display(radio_input2)
elif radio_input4.value=='Mortality':
    radio_input2 = widgets.RadioButtons(options=['Mortality'],value='Mortality')
    #display(radio_input2)

print("Extract Data")
print("Please select below if you want to work with ICU or Non-ICU data ?")
radio_input1 = widgets.RadioButtons(options=['ICU', 'Non-ICU'],value='ICU')
display(radio_input1)

print("Please select if you want to perform choosen prediction task for a specific disease.")
radio_input3 = widgets.RadioButtons(options=['No Disease Filter','Heart Failure','CKD','CAD','COPD'],value='CKD')
display(radio_input3)

Extract Data
Please select below if you want to work with ICU or Non-ICU data ?

RadioButtons(options=('ICU', 'Non-ICU'), value='ICU')
Please select if you want to perform choosen prediction task for a specific disease.

RadioButtons(index=2, options=('No Disease Filter', 'Heart Failure', 'CKD', 'CAD', 'COPD'), value='CKD')
In [ ]:
disease_label=""
time=0
label=radio_input4.value

if label=='Readmission':
    if radio_input2.value=='Custom':
        time=text1.value
    else:
        time=int(radio_input2.value.split()[0])
elif label=='Length of Stay':
    if radio_input2.value=='Custom':
        time=text1.value
    else:
        time=int(radio_input2.value.split()[4])

if label=='Phenotype':    
    if radio_input2.value=='Heart Failure in 30 days':
        label='Readmission'
        time=30
        disease_label='I50'
    elif radio_input2.value=='CAD in 30 days':
        label='Readmission'
        time=30
        disease_label='I25'
    elif radio_input2.value=='CKD in 30 days':
        label='Readmission'
        time=30
        disease_label='N18'
    elif radio_input2.value=='COPD in 30 days':
        label='Readmission'
        time=30
        disease_label='J44'
    
data_icu=radio_input1.value=="ICU"
data_mort=label=="Mortality"
data_admn=label=='Readmission'
data_los=label=='Length of Stay'
        

if (radio_input3.value=="Heart Failure"):
    icd_code='I50'
elif (radio_input3.value=="CKD"):
    icd_code='N18'
elif (radio_input3.value=="COPD"):
    icd_code='J44'
elif (radio_input3.value=="CAD"):
    icd_code='I25'
else:
    icd_code='No Disease Filter'

if version.value=='Version 1':
    version_path="mimiciv/1.0"
    cohort_output = day_intervals_cohort.extract_data(radio_input1.value,label,time,icd_code, root_dir,disease_label)
elif version.value=='Version 2':
    version_path="mimiciv/2.0"
    cohort_output = day_intervals_cohort_v2.extract_data(radio_input1.value,label,time,icd_code, root_dir,disease_label)

===========MIMIC-IV v2.0============
EXTRACTING FOR: | ICU | MORTALITY | ADMITTED DUE TO N18 | 0 |
[ MORTALITY LABELS FINISHED ]
[ COHORT SUCCESSFULLY SAVED ]
[ SUMMARY SUCCESSFULLY SAVED ]
Mortality FOR ICU DATA
# Admission Records: 16390
# Patients: 10276
# Positive cases: 1481
# Negative cases: 14909

2. FEATURE SELECTION¶

Features available for ICU data -

  • Diagnosis (https://mimic.mit.edu/docs/iv/modules/hosp/diagnoses_icd/)
  • Procedures (https://mimic.mit.edu/docs/iv/modules/icu/procedureevents/)
  • Medications (https://mimic.mit.edu/docs/iv/modules/icu/inputevents/)
  • Output Events (https://mimic.mit.edu/docs/iv/modules/icu/outputevents/)
  • Chart Events (https://mimic.mit.edu/docs/iv/modules/icu/chartevents/)

Features available for ICU data -

  • Diagnosis (https://mimic.mit.edu/docs/iv/modules/hosp/diagnoses_icd/)
  • Procedures (https://mimic.mit.edu/docs/iv/modules/hosp/procedures_icd/)
  • Medications (https://mimic.mit.edu/docs/iv/modules/hosp/prescriptions/)
  • Lab Events (https://mimic.mit.edu/docs/iv/modules/hosp/labevents/)

All features will be saved in ./data/features/

Please run below cell to select features

In [ ]:
print("Feature Selection")
if data_icu:
    print("Which Features you want to include for cohort?")
    check_input1 = widgets.Checkbox(description='Diagnosis', value=True)
    display(check_input1)
    check_input2 = widgets.Checkbox(description='Output Events')
    display(check_input2)
    check_input3 = widgets.Checkbox(description='Chart Events(Labs and Vitals)', value=True)
    display(check_input3)
    check_input4 = widgets.Checkbox(description='Procedures')
    display(check_input4)
    check_input5 = widgets.Checkbox(description='Medications', value=True)
    display(check_input5)
else:
    print("Which Features you want to include for cohort?")
    check_input1 = widgets.Checkbox(description='Diagnosis')
    display(check_input1)
    check_input2 = widgets.Checkbox(description='Labs')
    display(check_input2)
    check_input3 = widgets.Checkbox(description='Procedures')
    display(check_input3)
    check_input4 = widgets.Checkbox(description='Medications')
    display(check_input4)
print("**Please run below cell to extract selected features**")

Feature Selection
Which Features you want to include for cohort?

Checkbox(value=True, description='Diagnosis')
Checkbox(value=False, description='Output Events')
Checkbox(value=True, description='Chart Events(Labs and Vitals)')
Checkbox(value=False, description='Procedures')
Checkbox(value=True, description='Medications')
**Please run below cell to extract selected features**
In [ ]:
if data_icu:
    diag_flag=check_input1.value
    out_flag=check_input2.value
    chart_flag=check_input3.value
    proc_flag=check_input4.value
    med_flag=check_input5.value
    feature_icu(cohort_output, version_path,diag_flag,out_flag,chart_flag,proc_flag,med_flag)
else:
    diag_flag=check_input1.value
    lab_flag=check_input2.value
    proc_flag=check_input3.value
    med_flag=check_input4.value
    feature_nonicu(cohort_output, version_path,diag_flag,lab_flag,proc_flag,med_flag)

[EXTRACTING DIAGNOSIS DATA]
# unique ICD-9 codes 3738
# unique ICD-10 codes 5211
# unique ICD-10 codes (After converting ICD-9 to ICD-10) 5540
# unique ICD-10 codes (After clinical gruping ICD-10 codes) 1151
# Admissions:   16390
Total rows 378380

0it [00:00, ?it/s]
[SUCCESSFULLY SAVED DIAGNOSIS DATA]
[EXTRACTING CHART EVENTS DATA]

33it [03:11,  5.81s/it]

# Unique Events:   436
# Admissions:   16389
Total rows 19898308
[SUCCESSFULLY SAVED CHART EVENTS DATA]
[EXTRACTING MEDICATIONS DATA]
# of unique type of drug:  175
# Admissions:   15224
# Total rows 1236310
[SUCCESSFULLY SAVED MEDICATIONS DATA]
In [ ]:
df=pd.read_csv(root_dir+'/data/cohort/cohort_icu_mortality_0__N18.csv.gz',compression='gzip')
unique_values = {column: df[column].unique() for column in df.columns}
print(unique_values)

{'subject_id': array([10000980, 10002155, 10002430, ..., 19997473, 19998330, 19999625]), 'stay_id': array([39765666, 33685454, 31090461, ..., 33428243, 31199714, 31070865]), 'intime': array(['2189-06-27 08:42:00', '2129-08-04 12:45:00',
       '2130-09-24 00:50:00', ..., '2178-11-27 22:53:00',
       '2178-11-29 21:51:19', '2139-10-10 19:18:00'], dtype=object), 'outtime': array(['2189-06-27 20:38:27', '2129-08-10 17:02:38',
       '2130-09-27 22:13:41', ..., '2178-11-29 21:29:39',
       '2178-11-29 22:44:27', '2139-10-11 18:21:28'], dtype=object), 'Age': array([73, 80, 86, 72, 47, 82, 51, 66, 69, 89, 91, 70, 54, 77, 53, 87, 67,
       65, 83, 50, 58, 57, 78, 60, 63, 46, 64, 79, 88, 55, 62, 76, 85, 75,
       48, 84, 68, 40, 59, 34, 56, 71, 35, 74, 49, 44, 81, 41, 61, 45, 38,
       32, 37, 43, 25, 42, 30, 27, 33, 31, 52, 39, 23, 36, 28, 26, 20, 24,
       29, 22, 21]), 'gender': array(['F', 'M'], dtype=object), 'ethnicity': array(['BLACK/AFRICAN AMERICAN', 'WHITE', 'BLACK/CAPE VERDEAN', 'UNKNOWN',
       'ASIAN - SOUTH EAST ASIAN', 'BLACK/AFRICAN', 'PORTUGUESE',
       'HISPANIC/LATINO - CUBAN', 'OTHER',
       'HISPANIC/LATINO - PUERTO RICAN', 'WHITE - RUSSIAN',
       'ASIAN - ASIAN INDIAN', 'WHITE - EASTERN EUROPEAN',
       'ASIAN - CHINESE', 'ASIAN', 'HISPANIC OR LATINO',
       'UNABLE TO OBTAIN', 'WHITE - OTHER EUROPEAN',
       'PATIENT DECLINED TO ANSWER',
       'NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER',
       'AMERICAN INDIAN/ALASKA NATIVE',
       'HISPANIC/LATINO - CENTRAL AMERICAN', 'BLACK/CARIBBEAN ISLAND',
       'HISPANIC/LATINO - GUATEMALAN', 'HISPANIC/LATINO - DOMINICAN',
       'HISPANIC/LATINO - HONDURAN', 'HISPANIC/LATINO - SALVADORAN',
       'HISPANIC/LATINO - COLUMBIAN', 'ASIAN - KOREAN',
       'WHITE - BRAZILIAN', 'SOUTH AMERICAN', 'HISPANIC/LATINO - MEXICAN',
       'MULTIPLE RACE/ETHNICITY'], dtype=object), 'insurance': array(['Medicare', 'Other', 'Medicaid'], dtype=object), 'label': array([0, 1]), 'dod': array(['2193-08-26 00:00:00', '2131-03-10 00:00:00',
       '2130-01-11 00:00:00', ..., '2185-11-17 00:00:00',
       '2124-02-20 00:00:00', '2178-12-08 00:00:00'], dtype=object), 'hadm_id': array([26913865, 23822395, 28994087, ..., 21135114, 24096838, 25304202])}

3. CLINICAL GROUPING¶

Below you will have option to clinically group diagnosis and medications. Grouping medical codes will reduce dimensional space of features.

Default options selected below will group medical codes to reduce feature dimension space.

Please run below cell to select preprocessing for diferent features

In [ ]:
if data_icu:
    if diag_flag:
        print("Do you want to group ICD 10 DIAG codes ?")
        radio_input4 = widgets.RadioButtons(options=['Keep both ICD-9 and ICD-10 codes','Convert ICD-9 to ICD-10 codes','Convert ICD-9 to ICD-10 and group ICD-10 codes'],value='Convert ICD-9 to ICD-10 and group ICD-10 codes',layout={'width': '100%'})
        display(radio_input4)   
    
else:
    if diag_flag:
        print("Do you want to group ICD 10 DIAG codes ?")
        radio_input4 = widgets.RadioButtons(options=['Keep both ICD-9 and ICD-10 codes','Convert ICD-9 to ICD-10 codes','Convert ICD-9 to ICD-10 and group ICD-10 codes'],value='Convert ICD-9 to ICD-10 and group ICD-10 codes',layout={'width': '100%'})
        display(radio_input4)     
    if med_flag:
        print("Do you want to group Medication codes to use Non propietary names?")
        radio_input5 = widgets.RadioButtons(options=['Yes','No'],value='Yes',layout={'width': '100%'})
        display(radio_input5)
    if proc_flag:
        print("Which ICD codes for Procedures you want to keep in data?")
        radio_input6 = widgets.RadioButtons(options=['ICD-9 and ICD-10','ICD-10'],value='ICD-10',layout={'width': '100%'})
        display(radio_input6)
print("**Please run below cell to perform feature preprocessing**")

Do you want to group ICD 10 DIAG codes ?

RadioButtons(index=2, layout=Layout(width='100%'), options=('Keep both ICD-9 and ICD-10 codes', 'Convert ICD-9…
**Please run below cell to perform feature preprocessing**
In [ ]:
group_diag=False
group_med=False
group_proc=False
if data_icu:
    if diag_flag:
        group_diag=radio_input4.value
    preprocess_features_icu(cohort_output, diag_flag, group_diag,False,False,False,0,0)
else:
    if diag_flag:
        group_diag=radio_input4.value
    if med_flag:
        group_med=radio_input5.value
    if proc_flag:
        group_proc=radio_input6.value
    preprocess_features_hosp(cohort_output, diag_flag,proc_flag,med_flag,False,group_diag,group_med,group_proc,False,False,0,0)

[PROCESSING DIAGNOSIS DATA]
Total number of rows 362675
[SUCCESSFULLY SAVED DIAGNOSIS DATA]

4. SUMMARY OF FEATURES¶

This step will generate summary of all features extracted so far.
It will save summary files in ./data/summary/

  • These files provide summary about mean frequency of medical codes per admission.
  • It also provides total occurrence count of each medical code.
  • For labs and chart events it will also provide
    missing % which tells how many rows for a certain medical code has missing value.

Please use this information to further refine your cohort by selecting
which medical codes in each feature you want to keep and
which codes you would like to remove for downstream analysis tasks.

Please run below cell to generate summary files

In [ ]:
if data_icu:
    generate_summary_icu(diag_flag,proc_flag,med_flag,out_flag,chart_flag)
else:
    generate_summary_hosp(diag_flag,proc_flag,med_flag,lab_flag)

[GENERATING FEATURE SUMMARY]
[SUCCESSFULLY SAVED FEATURE SUMMARY]

5. Feature Selection¶

based on the files generated in previous step and other infromation gathered by you,
Please select which medical codes you want to include in this study.

Please run below cell to to select options for which features you want to perform feature selection.

  • Select Yes if you want to select a subset of medical codes for that feature and
    edit the corresponding feature file for it.
  • Select No if you want to keep all the codes in a feature.
In [ ]:
if data_icu:
    if diag_flag:
        print("Do you want to do Feature Selection for Diagnosis \n (If yes, please edit list of codes in ./data/summary/diag_features.csv)")
        radio_input4 = widgets.RadioButtons(options=['Yes','No'],value='No')
        display(radio_input4)       
    if med_flag:
        print("Do you want to do Feature Selection for Medication \n (If yes, please edit list of codes in ./data/summary/med_features.csv)")
        radio_input5 = widgets.RadioButtons(options=['Yes','No'],value='No')
        display(radio_input5)   
    if proc_flag:
        print("Do you want to do Feature Selection for Procedures \n (If yes, please edit list of codes in ./data/summary/proc_features.csv)")
        radio_input6 = widgets.RadioButtons(options=['Yes','No'],value='No')
        display(radio_input6)   
    if out_flag:
        print("Do you want to do Feature Selection for Output event \n (If yes, please edit list of codes in ./data/summary/out_features.csv)")
        radio_input7 = widgets.RadioButtons(options=['Yes','No'],value='No')
        display(radio_input7)  
    if chart_flag:
        print("Do you want to do Feature Selection for Chart events \n (If yes, please edit list of codes in ./data/summary/chart_features.csv)")
        radio_input8 = widgets.RadioButtons(options=['Yes','No'],value='No')
        display(radio_input8)  
else:
    if diag_flag:
        print("Do you want to do Feature Selection for Diagnosis \n (If yes, please edit list of codes in ./data/summary/diag_features.csv)")
        radio_input4 = widgets.RadioButtons(options=['Yes','No'],value='No')
        display(radio_input4)         
    if med_flag:
        print("Do you want to do Feature Selection for Medication \n (If yes, please edit list of codes in ./data/summary/med_features.csv)")
        radio_input5 = widgets.RadioButtons(options=['Yes','No'],value='No')
        display(radio_input5)   
    if proc_flag:
        print("Do you want to do Feature Selection for Procedures \n (If yes, please edit list of codes in ./data/summary/proc_features.csv)")
        radio_input6 = widgets.RadioButtons(options=['Yes','No'],value='No')
        display(radio_input6)   
    if lab_flag:
        print("Do you want to do Feature Selection for Labs \n (If yes, please edit list of codes in ./data/summary/lab_features.csv)")
        radio_input7 = widgets.RadioButtons(options=['Yes','No'],value='No')
        display(radio_input7)   
print("**Please run below cell to perform feature selection**")

Do you want to do Feature Selection for Diagnosis 
 (If yes, please edit list of codes in ./data/summary/diag_features.csv)

RadioButtons(index=1, options=('Yes', 'No'), value='No')
Do you want to do Feature Selection for Medication 
 (If yes, please edit list of codes in ./data/summary/med_features.csv)

RadioButtons(index=1, options=('Yes', 'No'), value='No')
Do you want to do Feature Selection for Chart events 
 (If yes, please edit list of codes in ./data/summary/chart_features.csv)

RadioButtons(index=1, options=('Yes', 'No'), value='No')
**Please run below cell to perform feature selection**
In [ ]:
select_diag=False
select_med=False
select_proc=False
select_lab=False
select_out=False
select_chart=False

if data_icu:
    if diag_flag:
        select_diag=radio_input4.value == 'Yes'
    if med_flag:
        select_med=radio_input5.value == 'Yes'
    if proc_flag:
        select_proc=radio_input6.value == 'Yes'
    if out_flag:
        select_out=radio_input7.value == 'Yes'
    if chart_flag:
        select_chart=radio_input8.value == 'Yes'
    features_selection_icu(cohort_output, diag_flag,proc_flag,med_flag,out_flag, chart_flag,select_diag,select_med,select_proc,select_out,select_chart)
else:
    if diag_flag:
        select_diag=radio_input4.value == 'Yes'
    if med_flag:
        select_med=radio_input5.value == 'Yes'
    if proc_flag:
        select_proc=radio_input6.value == 'Yes'
    if lab_flag:
        select_lab=radio_input7.value == 'Yes'
    features_selection_hosp(cohort_output, diag_flag,proc_flag,med_flag,lab_flag,select_diag,select_med,select_proc,select_lab)

6. CLEANING OF FEATURES¶

Below you will have option to to clean lab and chart events by performing outlier removal and unit conversion.

Outlier removal is performed to remove values higher than selected right threshold percentile and lower than selected left threshold percentile among all values for each itemid.

Please run below cell to select preprocessing for diferent features

In [ ]:
if data_icu:
    if chart_flag:
        print("Outlier removal in values of chart events ?")
        layout = widgets.Layout(width='100%', height='40px') #set width and height

        radio_input5 = widgets.RadioButtons(options=['No outlier detection','Impute Outlier (default:98)','Remove outliers (default:98)'],value='No outlier detection',layout=layout)
        display(radio_input5)
        outlier=widgets.IntSlider(
        value=98,
        min=90,
        max=99,
        step=1,
        disabled=False,layout={'width': '100%'}
        )
        left_outlier=widgets.IntSlider(
        value=0,
        min=0,
        max=10,
        step=1,
        disabled=False,layout={'width': '100%'}
        )
        #display(oulier)
        display(widgets.HBox([widgets.Label('Right Outlier Threshold',layout={'width': '150px'}), outlier]))
        display(widgets.HBox([widgets.Label('Left Outlier Threshold',layout={'width': '150px'}), left_outlier]))
    
else:      
    if lab_flag:
        print("Outlier removal in values of lab events ?")
        layout = widgets.Layout(width='100%', height='40px') #set width and height

        radio_input7 = widgets.RadioButtons(options=['No outlier detection','Impute Outlier (default:98)','Remove outliers (default:98)'],value='No outlier detection',layout=layout)
        display(radio_input7)
        outlier=widgets.IntSlider(
        value=98,
        min=90,
        max=99,
        step=1,
        disabled=False,layout={'width': '100%'}
        )
        left_outlier=widgets.IntSlider(
        value=0,
        min=0,
        max=10,
        step=1,
        disabled=False,layout={'width': '100%'}
        )
        #display(oulier)
        display(widgets.HBox([widgets.Label('Right Outlier Threshold',layout={'width': '150px'}), outlier]))
        display(widgets.HBox([widgets.Label('Left Outlier Threshold',layout={'width': '150px'}), left_outlier]))
print("**Please run below cell to perform feature preprocessing**")

Outlier removal in values of chart events ?

RadioButtons(layout=Layout(height='40px', width='100%'), options=('No outlier detection', 'Impute Outlier (def…
HBox(children=(Label(value='Right Outlier Threshold', layout=Layout(width='150px')), IntSlider(value=98, layou…
HBox(children=(Label(value='Left Outlier Threshold', layout=Layout(width='150px')), IntSlider(value=0, layout=…
**Please run below cell to perform feature preprocessing**
In [ ]:
thresh=0
if data_icu:
    if chart_flag:
        clean_chart=radio_input5.value!='No outlier detection'
        impute_outlier_chart=radio_input5.value=='Impute Outlier (default:98)'
        thresh=outlier.value
        left_thresh=left_outlier.value
    preprocess_features_icu(cohort_output, False, False,chart_flag,clean_chart,impute_outlier_chart,thresh,left_thresh)
else:
    if lab_flag:
        clean_lab=radio_input7.value!='No outlier detection'
        impute_outlier=radio_input7.value=='Impute Outlier (default:98)'
        thresh=outlier.value
        left_thresh=left_outlier.value
    preprocess_features_hosp(cohort_output, False,False,False,lab_flag,False,False,False,clean_lab,impute_outlier,thresh,left_thresh)

7. Time-Series Representation¶

In this section, please choose how you want to process and represent time-series data.

  • First option is to select the length of time-series data you want to include for this study. (Default is 72 hours)

  • Second option is to select bucket size which tells in what size time windows you want to divide your time-series.
    For example, if you select 2 bucket size, it wil aggregate data for every 2 hours and
    a time-series of length 24 hours will be represented as time-series with 12 time-windows
    where data for every 2 hours is agggregated from original raw time-series.

During this step, we will also save the time-series data in data dictionaries in the format that can be directly used for following deep learning analysis.

Imputation¶

You can also choose if you want to impute lab/chart values. The imputation will be done by froward fill and mean or median imputation.
Values will be forward fill first and if no value exists for that admission we will use mean or median value for the patient.

The data dictionaries will be saved in ./data/dict/

Please refer the readme to know the structure of data dictionaries.

Please run below cell to select time-series representation

In [ ]:
print("=======Time-series Data Represenation=======")

print("Length of data to be included for time-series prediction ?")
if(data_mort):
    radio_input8 = widgets.RadioButtons(options=['First 72 hours','First 48 hours','First 24 hours','Custom'],value='First 72 hours')
    display(radio_input8)
    text2=widgets.IntSlider(
    value=72,
    min=24,
    max=72,
    step=1,
    description='Fisrt',
    disabled=False
    )
    display(widgets.HBox([widgets.Label('Fisrt (in hours):',layout={'width': '150px'}), text2]))
elif(data_admn):
    radio_input8 = widgets.RadioButtons(options=['Last 72 hours','Last 48 hours','Last 24 hours','Custom'],value='Last 72 hours')
    display(radio_input8)
    text2=widgets.IntSlider(
    value=72,
    min=24,
    max=72,
    step=1,
    description='Last',
    disabled=False
    )
    display(widgets.HBox([widgets.Label('Last (in hours):',layout={'width': '150px'}), text2]))
elif(data_los):
    radio_input8 = widgets.RadioButtons(options=['First 12 hours','First 24 hours','Custom'],value='First 24 hours')
    display(radio_input8)
    text2=widgets.IntSlider(
    value=72,
    min=12,
    max=72,
    step=1,
    description='First',
    disabled=False
    )
    display(widgets.HBox([widgets.Label('Fisrt (in hours):',layout={'width': '150px'}), text2]))
    
    
print("What time bucket size you want to choose ?")
radio_input7 = widgets.RadioButtons(options=['1 hour','2 hour','3 hour','4 hour','5 hour','Custom'],value='1 hour')
display(radio_input7)
text1=widgets.IntSlider(
    value=1,
    min=1,
    max=6,
    step=1,
    disabled=False
    )
#display(text1)
display(widgets.HBox([widgets.Label('Bucket Size (in hours):',layout={'width': '150px'}), text1]))
print("Do you want to forward fill and mean or median impute lab/chart values to form continuous data signal?")
radio_impute = widgets.RadioButtons(options=['No Imputation', 'forward fill and mean','forward fill and median'],value='No Imputation')
display(radio_impute)   

radio_input6 = widgets.RadioButtons(options=['0 hours','2 hours','4 hours','6 hours'],value='0 hours')
if(data_mort):
    print("If you have choosen mortality prediction task, then what prediction window length you want to keep?")
    radio_input6 = widgets.RadioButtons(options=['2 hours','4 hours','6 hours','8 hours','Custom'],value='2 hours')
    display(radio_input6)
    text3=widgets.IntSlider(
    value=2,
    min=2,
    max=8,
    step=1,
    disabled=False
    )
    display(widgets.HBox([widgets.Label('Prediction window (in hours)',layout={'width': '180px'}), text3]))
print("**Please run below cell to perform time-series represenation and save in data dictionaries**")

=======Time-series Data Represenation=======
Length of data to be included for time-series prediction ?

RadioButtons(options=('First 72 hours', 'First 48 hours', 'First 24 hours', 'Custom'), value='First 72 hours')
HBox(children=(Label(value='Fisrt (in hours):', layout=Layout(width='150px')), IntSlider(value=72, description…
What time bucket size you want to choose ?

RadioButtons(options=('1 hour', '2 hour', '3 hour', '4 hour', '5 hour', 'Custom'), value='1 hour')
HBox(children=(Label(value='Bucket Size (in hours):', layout=Layout(width='150px')), IntSlider(value=1, max=6,…
Do you want to forward fill and mean or median impute lab/chart values to form continuous data signal?

RadioButtons(options=('No Imputation', 'forward fill and mean', 'forward fill and median'), value='No Imputati…
If you have choosen mortality prediction task, then what prediction window length you want to keep?

RadioButtons(options=('2 hours', '4 hours', '6 hours', '8 hours', 'Custom'), value='2 hours')
HBox(children=(Label(value='Prediction window (in hours)', layout=Layout(width='180px')), IntSlider(value=2, m…
**Please run below cell to perform time-series represenation and save in data dictionaries**
In [ ]:
if (radio_input6.value=='Custom'):
    predW=int(text3.value)
else:
    predW=int(radio_input6.value[0].strip())
if (radio_input7.value=='Custom'):
    bucket=int(text1.value)
else:
    bucket=int(radio_input7.value[0].strip())
if (radio_input8.value=='Custom'):
    include=int(text2.value)
else:
    include=int(radio_input8.value.split()[1])
if (radio_impute.value=='forward fill and mean'):
    impute='Mean'
elif (radio_impute.value=='forward fill and median'):
    impute='Median'
else:
    impute=False

if data_icu:
    gen=data_generation_icu.Generator(cohort_output,data_mort,data_admn,data_los,diag_flag,proc_flag,out_flag,chart_flag,med_flag,impute,include,bucket,predW)
    #gen=data_generation_icu.Generator(cohort_output,data_mort,diag_flag,False,False,chart_flag,False,impute,include,bucket,predW)
    #if chart_flag:
    #    gen=data_generation_icu.Generator(cohort_output,data_mort,False,False,False,chart_flag,False,impute,include,bucket,predW)
else:
    gen=data_generation.Generator(cohort_output,data_mort,data_admn,data_los,diag_flag,lab_flag,proc_flag,med_flag,impute,include,bucket,predW)

0it [00:00, ?it/s]
[ READ COHORT ]
[ ======READING DIAGNOSIS ]
[ ======READING CHART EVENTS ]

4it [01:30, 22.56s/it]

[ ======READING MEDICATIONS ]
[ READ ALL FEATURES ]
include_time 72
[ PROCESSED TIME SERIES TO EQUAL LENGTH  ]

100%|██████████| 72/72 [00:04<00:00, 15.88it/s]

bucket 1
[ PROCESSED TIME SERIES TO EQUAL TIME INTERVAL ]
72

100%|██████████| 5736/5736 [16:27<00:00,  5.81it/s]

[ SUCCESSFULLY SAVED DATA DICTIONARIES ]

8. Machine Learning Models¶

Below we provide options to select -

  • Type of machine learning model
  • Wheteher to concatenate or aggregate time-series features. For example, if the EHR data has collected value for Blood Pressure for one year over 4 time windows of 3 months each then,
    • Conactenate will concatenate all four values resulting in 4 different features for blood pressure,
    • Aggregate will aggreagte(mean) over four tiem windows resulting in one feature for blood pressure.
In [ ]:
print("=======Machine :earning Models=======")
radio_input5 = widgets.RadioButtons(options=['Logistic Regression','Random Forest','Gradient Bossting','Xgboost'],value='Gradient Bossting')
display(radio_input5)
print("Do you wnat to conactenate the time-series feature")
radio_input6 = widgets.RadioButtons(options=['Concatenate','Aggregate'],value='Concatenate')
display(radio_input6)
print("Please select below option for cross-validation")
radio_input7 = widgets.RadioButtons(options=['No CV','5-fold CV','10-fold CV'],value='5-fold CV')
display(radio_input7)
print("Do you want to do oversampling for minority calss ?")
radio_input8 = widgets.RadioButtons(options=['True','False'],value='True')
display(radio_input8)

=======Machine :earning Models=======

RadioButtons(index=2, options=('Logistic Regression', 'Random Forest', 'Gradient Bossting', 'Xgboost'), value=…
Do you wnat to conactenate the time-series feature

RadioButtons(options=('Concatenate', 'Aggregate'), value='Concatenate')
Please select below option for cross-validation

RadioButtons(index=1, options=('No CV', '5-fold CV', '10-fold CV'), value='5-fold CV')
Do you want to do oversampling for minority calss ?

RadioButtons(options=('True', 'False'), value='True')
In [ ]:
import matplotlib.pyplot as plt

df = pd.read_csv(root_dir+'/ethnicity_data.csv', delimiter=';')
column_names = ['num_patient', 'Ethnicity']
df.columns = column_names
df.plot(x='Ethnicity', y='num_patient', kind='bar', color='royalblue')
plt.xlabel('Ethnicity')
plt.ylabel('Number of Patients')
plt.title('Distribution of Ethnicity')
plt.show()
In [ ]:
if radio_input7.value=='No CV':
    cv=0
elif radio_input7.value=='5-fold CV':
    cv=int(5)
elif radio_input7.value=='10-fold CV':
    cv=int(10)

ml=ml_models.ML_models(data_icu,cv,radio_input5.value,concat=radio_input6.value=='Concatenate',oversampling=radio_input8.value=='True')

Total Samples 5736
Positive Samples 854
=============OVERSAMPLING===============
Total Samples 9764
Positive Samples 4882
=================== 0 FOLD=====================
train_hids 7808
X_df (7808, 41599)
y_df (7808,)
(7808, 41599)
(7808,)
test_hids 1952
X_df (1952, 41599)
y_df (1952,)
(1952, 41599)
(1952,)
===============MODEL TRAINING===============
BCE Loss: 0.26
AU-ROC: 1.00
AU-PRC: 0.99
AU-PRC Baaseline: 0.51
Accuracy: 0.96
Precision: 0.94
Recall: 0.99
Specificity: 0.93
NPV: 0.99
ECE: 0.06
MCE: 0.35
=================== 1 FOLD=====================
train_hids 7808
X_df (7808, 41599)
y_df (7808,)
(7808, 41599)
(7808,)
test_hids 1952
X_df (1952, 41599)
y_df (1952,)
(1952, 41599)
(1952,)
===============MODEL TRAINING===============
BCE Loss: 0.26
AU-ROC: 1.00
AU-PRC: 1.00
AU-PRC Baaseline: 0.49
Accuracy: 0.96
Precision: 0.93
Recall: 1.00
Specificity: 0.93
NPV: 1.00
ECE: 0.08
MCE: 0.58
=================== 2 FOLD=====================
train_hids 7808
X_df (7808, 41599)
y_df (7808,)
(7808, 41599)
(7808,)
test_hids 1952
X_df (1952, 41599)
y_df (1952,)
(1952, 41599)
(1952,)
===============MODEL TRAINING===============
BCE Loss: 0.25
AU-ROC: 1.00
AU-PRC: 1.00
AU-PRC Baaseline: 0.51
Accuracy: 0.96
Precision: 0.93
Recall: 1.00
Specificity: 0.92
NPV: 1.00
ECE: 0.08
MCE: 0.55
=================== 3 FOLD=====================
train_hids 7808
X_df (7808, 41599)
y_df (7808,)
(7808, 41599)
(7808,)
test_hids 1952
X_df (1952, 41599)
y_df (1952,)
(1952, 41599)
(1952,)
===============MODEL TRAINING===============
BCE Loss: 0.28
AU-ROC: 0.99
AU-PRC: 0.99
AU-PRC Baaseline: 0.51
Accuracy: 0.96
Precision: 0.93
Recall: 0.99
Specificity: 0.92
NPV: 0.99
ECE: 0.07
MCE: 0.48
=================== 4 FOLD=====================
train_hids 7808
X_df (7808, 41599)
y_df (7808,)
(7808, 41599)
(7808,)
test_hids 1952
X_df (1952, 41599)
y_df (1952,)
(1952, 41599)
(1952,)
===============MODEL TRAINING===============
BCE Loss: 0.25
AU-ROC: 1.00
AU-PRC: 1.00
AU-PRC Baaseline: 0.48
Accuracy: 0.96
Precision: 0.93
Recall: 1.00
Specificity: 0.93
NPV: 1.00
ECE: 0.08
MCE: 0.55

9. Deep Learning Models¶

  • Time-series LSTM and Time-series CNN which will only use time-series events like medications, charts, labs, output events to train model.

  • Hybrid LSTM and Hybrid CNN will use static data - diagnosis, demographic data aong with other time-series data to train model.

  • LSTM with Attention model will use attention layer to rank the important features and learn to predict output. It will use both static and time-series data.

Go to ./model/parameter.py and define all variables needed for model building and training

Please run below cell to select which model to use

In [ ]:
radio_input6=widgets.RadioButtons(options=['Time-series LSTM','Time-series CNN','Hybrid LSTM','Hybrid CNN'],value='Time-series LSTM')
display(radio_input6)
print("Please select below option for cross-validation")
radio_input7 = widgets.RadioButtons(options=['No CV','5-fold CV','10-fold CV'],value='5-fold CV')
display(radio_input7)
print("Do you want to do oversampling for minority calss ?")
radio_input8 = widgets.RadioButtons(options=['True','False'],value='True')
display(radio_input8)

RadioButtons(options=('Time-series LSTM', 'Time-series CNN', 'Hybrid LSTM', 'Hybrid CNN'), value='Time-series …
Please select below option for cross-validation

RadioButtons(index=1, options=('No CV', '5-fold CV', '10-fold CV'), value='5-fold CV')
Do you want to do oversampling for minority calss ?

RadioButtons(options=('True', 'False'), value='True')
In [ ]:
if radio_input7.value=='No CV':
    cv=0
elif radio_input7.value=='5-fold CV':
    cv=int(5)
elif radio_input7.value=='10-fold CV':
    cv=int(10)
    
if data_icu:
    model=dl_train.DL_models(data_icu,diag_flag,proc_flag,out_flag,chart_flag,med_flag,False,radio_input6.value,cv,oversampling=radio_input8.value=='True',model_name='attn_icu_read',train=True)
else:
    model=dl_train.DL_models(data_icu,diag_flag,proc_flag,False,False,med_flag,lab_flag,radio_input6.value,cv,oversampling=radio_input8.value=='True',model_name='attn_icu_read',train=True)

===============MODEL TRAINING===============
Total Samples 5736
Positive Samples 854
=============OVERSAMPLING===============
Total Samples 9764
Positive Samples 4882
[ MODEL CREATED ]
LSTMBase(
  (med): ValEmbed(
    (codeEmbed): BatchNorm1d(149, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (fc): Linear(in_features=149, out_features=152, bias=True)
  )
  (chart): ValEmbed(
    (codeEmbed): BatchNorm1d(415, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (fc): Linear(in_features=415, out_features=152, bias=True)
  )
  (cond): StatEmbed(
    (codeEmbed): Embedding(987, 52)
    (fc): Linear(in_features=51324, out_features=152, bias=True)
  )
  (ethEmbed): Embedding(34, 152, padding_idx=0)
  (genderEmbed): Embedding(3, 152, padding_idx=0)
  (ageEmbed): Embedding(72, 152, padding_idx=0)
  (insEmbed): Embedding(4, 152, padding_idx=0)
  (embedfc): Linear(in_features=1064, out_features=152, bias=True)
  (rnn): LSTM(152, 256, num_layers=2, batch_first=True)
  (fc1): Linear(in_features=256, out_features=128, bias=True)
  (fc2): Linear(in_features=128, out_features=1, bias=True)
)
=================== 0 FOLD=====================
======= EPOCH 0.0 ========
BCE Loss: 1.19
AU-ROC: 0.75
AU-PRC: 0.34
AU-PRC Baaseline: 0.14
Accuracy: 0.63
Precision: 0.23
Recall: 0.75
Specificity: 0.61
NPV: 0.94
ECE: 0.32
MCE: 0.43
======= VALIDATION ========
BCE Loss: 1.07
AU-ROC: 0.82
AU-PRC: 0.81
AU-PRC Baaseline: 0.45
Accuracy: 0.74
Precision: 0.78
Recall: 0.60
Specificity: 0.86
NPV: 0.72
ECE: 0.07
MCE: 0.18
Validation results improved
Updating Model
======= EPOCH 1.0 ========
BCE Loss: 0.85
AU-ROC: 0.89
AU-PRC: 0.58
AU-PRC Baaseline: 0.14
Accuracy: 0.79
Precision: 0.38
Recall: 0.85
Specificity: 0.78
NPV: 0.97
ECE: 0.20
MCE: 0.45
======= VALIDATION ========
BCE Loss: 1.02
AU-ROC: 0.83
AU-PRC: 0.81
AU-PRC Baaseline: 0.45
Accuracy: 0.76
Precision: 0.77
Recall: 0.68
Specificity: 0.83
NPV: 0.76
ECE: 0.02
MCE: 0.17
Validation results improved
Updating Model
======= EPOCH 2.0 ========
BCE Loss: 0.75
AU-ROC: 0.91
AU-PRC: 0.58
AU-PRC Baaseline: 0.14
Accuracy: 0.80
Precision: 0.40
Recall: 0.90
Specificity: 0.79
NPV: 0.98
ECE: 0.17
MCE: 0.46
======= VALIDATION ========
BCE Loss: 1.13
AU-ROC: 0.81
AU-PRC: 0.79
AU-PRC Baaseline: 0.45
Accuracy: 0.76
Precision: 0.78
Recall: 0.65
Specificity: 0.85
NPV: 0.75
ECE: 0.07
MCE: 0.11
No improvement in Validation results
======= EPOCH 3.0 ========
BCE Loss: 0.67
AU-ROC: 0.92
AU-PRC: 0.59
AU-PRC Baaseline: 0.14
Accuracy: 0.84
Precision: 0.45
Recall: 0.91
Specificity: 0.83
NPV: 0.98
ECE: 0.15
MCE: 0.46
======= VALIDATION ========
BCE Loss: 1.18
AU-ROC: 0.80
AU-PRC: 0.77
AU-PRC Baaseline: 0.45
Accuracy: 0.74
Precision: 0.69
Recall: 0.78
Specificity: 0.71
NPV: 0.80
ECE: 0.09
MCE: 0.17
No improvement in Validation results
STOPPING THE TRAINING BECAUSE VALIDATION ERROR DID NOT IMPROVE FOR 2.0 EPOCHS
======= TESTING ========
BCE Loss: 0.87
AU-ROC: 0.89
AU-PRC: 0.87
AU-PRC Baaseline: 0.52
Accuracy: 0.80
Precision: 0.75
Recall: 0.93
Specificity: 0.67
NPV: 0.90
ECE: 0.07
MCE: 0.23
[ MODEL CREATED ]
LSTMBase(
  (med): ValEmbed(
    (codeEmbed): BatchNorm1d(149, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (fc): Linear(in_features=149, out_features=152, bias=True)
  )
  (chart): ValEmbed(
    (codeEmbed): BatchNorm1d(415, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (fc): Linear(in_features=415, out_features=152, bias=True)
  )
  (cond): StatEmbed(
    (codeEmbed): Embedding(987, 52)
    (fc): Linear(in_features=51324, out_features=152, bias=True)
  )
  (ethEmbed): Embedding(34, 152, padding_idx=0)
  (genderEmbed): Embedding(3, 152, padding_idx=0)
  (ageEmbed): Embedding(72, 152, padding_idx=0)
  (insEmbed): Embedding(4, 152, padding_idx=0)
  (embedfc): Linear(in_features=1064, out_features=152, bias=True)
  (rnn): LSTM(152, 256, num_layers=2, batch_first=True)
  (fc1): Linear(in_features=256, out_features=128, bias=True)
  (fc2): Linear(in_features=128, out_features=1, bias=True)
)
=================== 1 FOLD=====================
======= EPOCH 0.0 ========
BCE Loss: 1.19
AU-ROC: 0.75
AU-PRC: 0.33
AU-PRC Baaseline: 0.13
Accuracy: 0.70
Precision: 0.26
Recall: 0.67
Specificity: 0.71
NPV: 0.93
ECE: 0.30
MCE: 0.58
======= VALIDATION ========
BCE Loss: 1.12
AU-ROC: 0.81
AU-PRC: 0.80
AU-PRC Baaseline: 0.50
Accuracy: 0.70
Precision: 0.69
Recall: 0.75
Specificity: 0.66
NPV: 0.72
ECE: 0.12
MCE: 0.26
Validation results improved
Updating Model
======= EPOCH 1.0 ========
BCE Loss: 0.85
AU-ROC: 0.89
AU-PRC: 0.57
AU-PRC Baaseline: 0.13
Accuracy: 0.80
Precision: 0.39
Recall: 0.84
Specificity: 0.80
NPV: 0.97
ECE: 0.20
MCE: 0.42
======= VALIDATION ========
BCE Loss: 1.07
AU-ROC: 0.81
AU-PRC: 0.81
AU-PRC Baaseline: 0.50
Accuracy: 0.73
Precision: 0.71
Recall: 0.78
Specificity: 0.68
NPV: 0.76
ECE: 0.04
MCE: 0.09
Validation results improved
Updating Model
======= EPOCH 2.0 ========
BCE Loss: 0.70
AU-ROC: 0.92
AU-PRC: 0.63
AU-PRC Baaseline: 0.13
Accuracy: 0.84
Precision: 0.44
Recall: 0.91
Specificity: 0.83
NPV: 0.98
ECE: 0.16
MCE: 0.47
======= VALIDATION ========
BCE Loss: 1.23
AU-ROC: 0.80
AU-PRC: 0.79
AU-PRC Baaseline: 0.50
Accuracy: 0.73
Precision: 0.72
Recall: 0.77
Specificity: 0.69
NPV: 0.75
ECE: 0.12
MCE: 0.36
No improvement in Validation results
======= EPOCH 3.0 ========
BCE Loss: 0.61
AU-ROC: 0.94
AU-PRC: 0.70
AU-PRC Baaseline: 0.13
Accuracy: 0.86
Precision: 0.48
Recall: 0.91
Specificity: 0.85
NPV: 0.98
ECE: 0.13
MCE: 0.40
======= VALIDATION ========
BCE Loss: 1.42
AU-ROC: 0.80
AU-PRC: 0.80
AU-PRC Baaseline: 0.50
Accuracy: 0.71
Precision: 0.73
Recall: 0.66
Specificity: 0.76
NPV: 0.68
ECE: 0.16
MCE: 0.43
No improvement in Validation results
STOPPING THE TRAINING BECAUSE VALIDATION ERROR DID NOT IMPROVE FOR 2.0 EPOCHS
======= TESTING ========
BCE Loss: 0.90
AU-ROC: 0.89
AU-PRC: 0.88
AU-PRC Baaseline: 0.50
Accuracy: 0.82
Precision: 0.80
Recall: 0.85
Specificity: 0.80
NPV: 0.84
ECE: 0.05
MCE: 0.10
[ MODEL CREATED ]
LSTMBase(
  (med): ValEmbed(
    (codeEmbed): BatchNorm1d(149, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (fc): Linear(in_features=149, out_features=152, bias=True)
  )
  (chart): ValEmbed(
    (codeEmbed): BatchNorm1d(415, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (fc): Linear(in_features=415, out_features=152, bias=True)
  )
  (cond): StatEmbed(
    (codeEmbed): Embedding(987, 52)
    (fc): Linear(in_features=51324, out_features=152, bias=True)
  )
  (ethEmbed): Embedding(34, 152, padding_idx=0)
  (genderEmbed): Embedding(3, 152, padding_idx=0)
  (ageEmbed): Embedding(72, 152, padding_idx=0)
  (insEmbed): Embedding(4, 152, padding_idx=0)
  (embedfc): Linear(in_features=1064, out_features=152, bias=True)
  (rnn): LSTM(152, 256, num_layers=2, batch_first=True)
  (fc1): Linear(in_features=256, out_features=128, bias=True)
  (fc2): Linear(in_features=128, out_features=1, bias=True)
)
=================== 2 FOLD=====================
======= EPOCH 0.0 ========
BCE Loss: 1.20
AU-ROC: 0.74
AU-PRC: 0.31
AU-PRC Baaseline: 0.13
Accuracy: 0.67
Precision: 0.24
Recall: 0.68
Specificity: 0.67
NPV: 0.93
ECE: 0.30
MCE: 0.43
======= VALIDATION ========
BCE Loss: 1.02
AU-ROC: 0.84
AU-PRC: 0.85
AU-PRC Baaseline: 0.51
Accuracy: 0.74
Precision: 0.76
Recall: 0.73
Specificity: 0.76
NPV: 0.73
ECE: 0.04
MCE: 0.08
Validation results improved
Updating Model
======= EPOCH 1.0 ========
BCE Loss: 0.83
AU-ROC: 0.89
AU-PRC: 0.55
AU-PRC Baaseline: 0.13
Accuracy: 0.81
Precision: 0.39
Recall: 0.84
Specificity: 0.80
NPV: 0.97
ECE: 0.19
MCE: 0.46
======= VALIDATION ========
BCE Loss: 1.00
AU-ROC: 0.84
AU-PRC: 0.85
AU-PRC Baaseline: 0.51
Accuracy: 0.75
Precision: 0.77
Recall: 0.72
Specificity: 0.78
NPV: 0.73
ECE: 0.04
MCE: 0.12
Validation results improved
Updating Model
======= EPOCH 2.0 ========
BCE Loss: 0.70
AU-ROC: 0.92
AU-PRC: 0.60
AU-PRC Baaseline: 0.13
Accuracy: 0.84
Precision: 0.45
Recall: 0.89
Specificity: 0.84
NPV: 0.98
ECE: 0.15
MCE: 0.43
======= VALIDATION ========
BCE Loss: 1.20
AU-ROC: 0.82
AU-PRC: 0.84
AU-PRC Baaseline: 0.51
Accuracy: 0.74
Precision: 0.79
Recall: 0.67
Specificity: 0.82
NPV: 0.71
ECE: 0.14
MCE: 0.31
No improvement in Validation results
======= EPOCH 3.0 ========
BCE Loss: 0.66
AU-ROC: 0.93
AU-PRC: 0.61
AU-PRC Baaseline: 0.13
Accuracy: 0.84
Precision: 0.44
Recall: 0.91
Specificity: 0.83
NPV: 0.98
ECE: 0.14
MCE: 0.39
======= VALIDATION ========
BCE Loss: 1.20
AU-ROC: 0.82
AU-PRC: 0.84
AU-PRC Baaseline: 0.51
Accuracy: 0.72
Precision: 0.71
Recall: 0.76
Specificity: 0.69
NPV: 0.73
ECE: 0.13
MCE: 0.29
No improvement in Validation results
STOPPING THE TRAINING BECAUSE VALIDATION ERROR DID NOT IMPROVE FOR 2.0 EPOCHS
======= TESTING ========
BCE Loss: 0.89
AU-ROC: 0.89
AU-PRC: 0.89
AU-PRC Baaseline: 0.50
Accuracy: 0.81
Precision: 0.75
Recall: 0.90
Specificity: 0.71
NPV: 0.88
ECE: 0.08
MCE: 0.29
[ MODEL CREATED ]
LSTMBase(
  (med): ValEmbed(
    (codeEmbed): BatchNorm1d(149, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (fc): Linear(in_features=149, out_features=152, bias=True)
  )
  (chart): ValEmbed(
    (codeEmbed): BatchNorm1d(415, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (fc): Linear(in_features=415, out_features=152, bias=True)
  )
  (cond): StatEmbed(
    (codeEmbed): Embedding(987, 52)
    (fc): Linear(in_features=51324, out_features=152, bias=True)
  )
  (ethEmbed): Embedding(34, 152, padding_idx=0)
  (genderEmbed): Embedding(3, 152, padding_idx=0)
  (ageEmbed): Embedding(72, 152, padding_idx=0)
  (insEmbed): Embedding(4, 152, padding_idx=0)
  (embedfc): Linear(in_features=1064, out_features=152, bias=True)
  (rnn): LSTM(152, 256, num_layers=2, batch_first=True)
  (fc1): Linear(in_features=256, out_features=128, bias=True)
  (fc2): Linear(in_features=128, out_features=1, bias=True)
)
=================== 3 FOLD=====================
======= EPOCH 0.0 ========
BCE Loss: 1.21
AU-ROC: 0.74
AU-PRC: 0.31
AU-PRC Baaseline: 0.13
Accuracy: 0.68
Precision: 0.24
Recall: 0.69
Specificity: 0.68
NPV: 0.94
ECE: 0.31
MCE: 0.57
======= VALIDATION ========
BCE Loss: 1.00
AU-ROC: 0.85
AU-PRC: 0.85
AU-PRC Baaseline: 0.52
Accuracy: 0.77
Precision: 0.77
Recall: 0.81
Specificity: 0.74
NPV: 0.78
ECE: 0.06
MCE: 0.17
Validation results improved
Updating Model
======= EPOCH 1.0 ========
BCE Loss: 0.87
AU-ROC: 0.88
AU-PRC: 0.54
AU-PRC Baaseline: 0.13
Accuracy: 0.81
Precision: 0.39
Recall: 0.83
Specificity: 0.81
NPV: 0.97
ECE: 0.19
MCE: 0.45
======= VALIDATION ========
BCE Loss: 1.00
AU-ROC: 0.84
AU-PRC: 0.86
AU-PRC Baaseline: 0.52
Accuracy: 0.76
Precision: 0.81
Recall: 0.71
Specificity: 0.82
NPV: 0.72
ECE: 0.06
MCE: 0.12
Validation results improved
Updating Model
======= EPOCH 2.0 ========
BCE Loss: 0.70
AU-ROC: 0.92
AU-PRC: 0.63
AU-PRC Baaseline: 0.13
Accuracy: 0.83
Precision: 0.42
Recall: 0.89
Specificity: 0.82
NPV: 0.98
ECE: 0.16
MCE: 0.49
======= VALIDATION ========
BCE Loss: 1.11
AU-ROC: 0.83
AU-PRC: 0.85
AU-PRC Baaseline: 0.52
Accuracy: 0.76
Precision: 0.82
Recall: 0.68
Specificity: 0.83
NPV: 0.71
ECE: 0.08
MCE: 0.24
No improvement in Validation results
======= EPOCH 3.0 ========
BCE Loss: 0.63
AU-ROC: 0.93
AU-PRC: 0.66
AU-PRC Baaseline: 0.13
Accuracy: 0.85
Precision: 0.46
Recall: 0.91
Specificity: 0.84
NPV: 0.98
ECE: 0.15
MCE: 0.49
======= VALIDATION ========
BCE Loss: 1.64
AU-ROC: 0.81
AU-PRC: 0.84
AU-PRC Baaseline: 0.52
Accuracy: 0.74
Precision: 0.80
Recall: 0.66
Specificity: 0.83
NPV: 0.69
ECE: 0.18
MCE: 0.26
No improvement in Validation results
STOPPING THE TRAINING BECAUSE VALIDATION ERROR DID NOT IMPROVE FOR 2.0 EPOCHS
======= TESTING ========
BCE Loss: 1.03
AU-ROC: 0.88
AU-PRC: 0.88
AU-PRC Baaseline: 0.51
Accuracy: 0.83
Precision: 0.83
Recall: 0.84
Specificity: 0.82
NPV: 0.84
ECE: 0.09
MCE: 0.14
[ MODEL CREATED ]
LSTMBase(
  (med): ValEmbed(
    (codeEmbed): BatchNorm1d(149, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (fc): Linear(in_features=149, out_features=152, bias=True)
  )
  (chart): ValEmbed(
    (codeEmbed): BatchNorm1d(415, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    (fc): Linear(in_features=415, out_features=152, bias=True)
  )
  (cond): StatEmbed(
    (codeEmbed): Embedding(987, 52)
    (fc): Linear(in_features=51324, out_features=152, bias=True)
  )
  (ethEmbed): Embedding(34, 152, padding_idx=0)
  (genderEmbed): Embedding(3, 152, padding_idx=0)
  (ageEmbed): Embedding(72, 152, padding_idx=0)
  (insEmbed): Embedding(4, 152, padding_idx=0)
  (embedfc): Linear(in_features=1064, out_features=152, bias=True)
  (rnn): LSTM(152, 256, num_layers=2, batch_first=True)
  (fc1): Linear(in_features=256, out_features=128, bias=True)
  (fc2): Linear(in_features=128, out_features=1, bias=True)
)
=================== 4 FOLD=====================
======= EPOCH 0.0 ========
BCE Loss: 1.19
AU-ROC: 0.75
AU-PRC: 0.31
AU-PRC Baaseline: 0.13
Accuracy: 0.66
Precision: 0.24
Recall: 0.72
Specificity: 0.65
NPV: 0.94
ECE: 0.31
MCE: 0.55
======= VALIDATION ========
BCE Loss: 1.06
AU-ROC: 0.82
AU-PRC: 0.83
AU-PRC Baaseline: 0.51
Accuracy: 0.76
Precision: 0.77
Recall: 0.74
Specificity: 0.77
NPV: 0.74
ECE: 0.05
MCE: 0.14
Validation results improved
Updating Model
======= EPOCH 1.0 ========
BCE Loss: 0.83
AU-ROC: 0.89
AU-PRC: 0.55
AU-PRC Baaseline: 0.13
Accuracy: 0.80
Precision: 0.38
Recall: 0.86
Specificity: 0.79
NPV: 0.97
ECE: 0.20
MCE: 0.48
======= VALIDATION ========
BCE Loss: 1.12
AU-ROC: 0.80
AU-PRC: 0.81
AU-PRC Baaseline: 0.51
Accuracy: 0.73
Precision: 0.82
Recall: 0.60
Specificity: 0.86
NPV: 0.67
ECE: 0.09
MCE: 0.15
No improvement in Validation results
======= EPOCH 2.0 ========
BCE Loss: 0.70
AU-ROC: 0.92
AU-PRC: 0.60
AU-PRC Baaseline: 0.13
Accuracy: 0.83
Precision: 0.43
Recall: 0.89
Specificity: 0.82
NPV: 0.98
ECE: 0.16
MCE: 0.47
======= VALIDATION ========
BCE Loss: 1.49
AU-ROC: 0.76
AU-PRC: 0.76
AU-PRC Baaseline: 0.51
Accuracy: 0.71
Precision: 0.78
Recall: 0.60
Specificity: 0.82
NPV: 0.66
ECE: 0.15
MCE: 0.27
No improvement in Validation results
STOPPING THE TRAINING BECAUSE VALIDATION ERROR DID NOT IMPROVE FOR 2.0 EPOCHS
======= TESTING ========
BCE Loss: 0.98
AU-ROC: 0.87
AU-PRC: 0.86
AU-PRC Baaseline: 0.49
Accuracy: 0.80
Precision: 0.78
Recall: 0.82
Specificity: 0.77
NPV: 0.82
ECE: 0.06
MCE: 0.14

10. Running BEHRT¶

Below we integrate the implementation of BEHRT in our pipeline. We perform pre-procesing needed to run BEHRT model. https://github.com/deepmedicine/BEHRT

Few things to note before running BEHRT -

  • The numerical values are binned into quantiles.
  • BEHRT has recommended maximum number of events per sample to be 512. So feature selection is important so that number of events per sample does not exceed 512.
  • The model is quite computationally heavy so it requires a GPU.

The output files for BEHRT will be saved in ./data/behrt/ folder

Please run below cell to to pre-process and run BEHRT on the selected cohort

In [ ]:
%pwd
Out[ ]:
'/home/csgrad/thapar/MIMIC_IV'
In [ ]:
importlib.reload(tokenization)
from tokenization import *
In [ ]:
behrt_instance = BEHRT_models(data_icu, diag_flag, proc_flag, out_flag, chart_flag, med_flag, False)

if data_icu:
    tokenized_src, tokenized_age, tokenized_gender, tokenized_ethni, tokenized_ins, tokenized_labels = behrt_instance.tokenization()
else:
    tokenized_src, tokenized_age, tokenized_gender, tokenized_ethni, tokenized_ins, tokenized_labels = behrt_instance.tokenization()

  0%|          | 2/5736 [00:00<05:45, 16.62it/s]
STARTING READING FILES.

100%|██████████| 5736/5736 [04:49<00:00, 19.81it/s]

FINISHED READING FILES. 


  0%|          | 0/5736 [00:00<?, ?it/s]
STARTING TOKENIZATION.

100%|██████████| 5736/5736 [07:03<00:00, 13.53it/s]

FINISHED TOKENIZATION. 

FINAL COHORT STATISTICS: 
1 Positive samples.
23 Negative samples.

8 Female samples.
16 Male samples.

17 WHITE samples.
2 BLACK/AFRICAN AMERICAN samples.
0 BLACK/CAPE VERDEAN samples.
0 UNKNOWN samples.
0 BLACK/AFRICAN samples.
1 HISPANIC/LATINO - PUERTO RICAN samples.
0 WHITE - RUSSIAN samples.
0 WHITE - EASTERN EUROPEAN samples.
0 ASIAN - CHINESE samples.
1 ASIAN samples.
0 OTHER samples.
1 HISPANIC OR LATINO samples.
0 ASIAN - ASIAN INDIAN samples.
0 WHITE - OTHER EUROPEAN samples.
1 UNABLE TO OBTAIN samples.
0 NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER samples.
0 HISPANIC/LATINO - CENTRAL AMERICAN samples.
0 HISPANIC/LATINO - GUATEMALAN samples.
0 PORTUGUESE samples.
0 PATIENT DECLINED TO ANSWER samples.
1 HISPANIC/LATINO - DOMINICAN samples.
0 HISPANIC/LATINO - HONDURAN samples.
0 BLACK/CARIBBEAN ISLAND samples.
0 ASIAN - KOREAN samples.
0 ASIAN - SOUTH EAST ASIAN samples.
0 WHITE - BRAZILIAN samples.
0 HISPANIC/LATINO - CUBAN samples.
0 AMERICAN INDIAN/ALASKA NATIVE samples.
0 HISPANIC/LATINO - COLUMBIAN samples.
0 HISPANIC/LATINO - SALVADORAN samples.
0 SOUTH AMERICAN samples.
0 HISPANIC/LATINO - MEXICAN samples.
0 MULTIPLE RACE/ETHNICITY samples.


8 Other samples.
13 Medicare samples.
3 Medicaid samples.
In [ ]:
import numpy as np
tokenized_src.replace(np.nan, 0, inplace=True)
tokenized_age.replace(np.nan, 0, inplace=True)
tokenized_gender.replace(np.nan, 0, inplace=True)
tokenized_ethni.replace(np.nan, 0, inplace=True)
tokenized_ins.replace(np.nan, 0, inplace=True)
In [ ]:
tokenized_labels
Out[ ]:
0
0 0
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
10 0
11 0
12 0
13 0
14 1
15 0
16 0
17 0
18 0
19 0
20 0
21 0
22 0
23 0
In [ ]:
train_behrt_instance = behrt_train.train_behrt(tokenized_src, tokenized_age, tokenized_gender, tokenized_ethni, tokenized_ins, tokenized_labels)

Epoch n0
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TOTAL LOSS 0.6541979312896729
TRAIN 0.7120203375816345	0.8943543434143066 secs

EVAL 0.6541979312896729	0.03489565849304199 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n1
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TOTAL LOSS 0.6224567890167236
TRAIN 0.6781177520751953	0.8696351051330566 secs

EVAL 0.6224567890167236	0.03489851951599121 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n2
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TOTAL LOSS 0.5922495126724243
TRAIN 0.6459133625030518	0.8678741455078125 secs

EVAL 0.5922495126724243	0.03371095657348633 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n3
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TOTAL LOSS 0.5634900331497192
TRAIN 0.6487592458724976	0.8850581645965576 secs

EVAL 0.5634900331497192	0.03482222557067871 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n4
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TOTAL LOSS 0.5361005067825317
TRAIN 0.6063593029975891	0.8793370723724365 secs

EVAL 0.5361005067825317	0.03580307960510254 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n5
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TOTAL LOSS 0.5100816488265991
TRAIN 0.5823937654495239	0.8692107200622559 secs

EVAL 0.5100816488265991	0.03383588790893555 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n6
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TOTAL LOSS 0.4854029417037964
TRAIN 0.5641120672225952	0.8856685161590576 secs

EVAL 0.4854029417037964	0.03404092788696289 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n7
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TOTAL LOSS 0.4620403051376343
TRAIN 0.5506670475006104	0.8791823387145996 secs

EVAL 0.4620403051376343	0.03358745574951172 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n8
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TOTAL LOSS 0.4399416148662567
TRAIN 0.5183223485946655	0.8547811508178711 secs

EVAL 0.4399416148662567	0.03381180763244629 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n9
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TOTAL LOSS 0.419057160615921
TRAIN 0.5235449075698853	0.8506615161895752 secs

EVAL 0.419057160615921	0.03370213508605957 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n10
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TOTAL LOSS 0.399332731962204
TRAIN 0.5038314461708069	0.8565645217895508 secs

EVAL 0.399332731962204	0.03386545181274414 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n11
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TOTAL LOSS 0.3807169795036316
TRAIN 0.48733094334602356	0.8428483009338379 secs

EVAL 0.3807169795036316	0.033669233322143555 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n12
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TOTAL LOSS 0.3631266951560974
TRAIN 0.47348809242248535	0.8341624736785889 secs

EVAL 0.3631266951560974	0.03348660469055176 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n13
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TOTAL LOSS 0.34658193588256836
TRAIN 0.4561091959476471	0.8561708927154541 secs

EVAL 0.34658193588256836	0.03341317176818848 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n14
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TRAIN 0.4354802668094635	0.8720560073852539 secs

EVAL 0.3310390114784241	0.033599138259887695 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n15
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TOTAL LOSS 0.31645435094833374
TRAIN 0.4212636947631836	0.8774120807647705 secs

EVAL 0.31645435094833374	0.033777713775634766 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n16
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TOTAL LOSS 0.3027656376361847
TRAIN 0.405764102935791	0.8898518085479736 secs

EVAL 0.3027656376361847	0.0353236198425293 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n17
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TOTAL LOSS 0.2899276614189148
TRAIN 0.39395639300346375	0.9180610179901123 secs

EVAL 0.2899276614189148	0.035063743591308594 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n18
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TOTAL LOSS 0.27787652611732483
TRAIN 0.38573387265205383	0.884472131729126 secs

EVAL 0.27787652611732483	0.03445863723754883 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n19
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TOTAL LOSS 0.2665179371833801
TRAIN 0.3758006691932678	0.8656806945800781 secs

EVAL 0.2665179371833801	0.0350489616394043 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n20
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TOTAL LOSS 0.25589126348495483
TRAIN 0.36928054690361023	0.875849723815918 secs

EVAL 0.25589126348495483	0.03477621078491211 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n21
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TOTAL LOSS 0.24591641128063202
TRAIN 0.35326069593429565	0.8641908168792725 secs

EVAL 0.24591641128063202	0.035511016845703125 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n22
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TOTAL LOSS 0.23652136325836182
TRAIN 0.3565777838230133	0.8394687175750732 secs

EVAL 0.23652136325836182	0.03496742248535156 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n23
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TOTAL LOSS 0.227707177400589
TRAIN 0.3381546139717102	0.8328571319580078 secs

EVAL 0.227707177400589	0.03508138656616211 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n24
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TOTAL LOSS 0.2193988561630249
TRAIN 0.3325682282447815	0.8494980335235596 secs

EVAL 0.2193988561630249	0.03509378433227539 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n25
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TOTAL LOSS 0.2115633487701416
TRAIN 0.3336904048919678	0.851107120513916 secs

EVAL 0.2115633487701416	0.03457474708557129 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n26
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TOTAL LOSS 0.204213947057724
TRAIN 0.3195812702178955	0.8377125263214111 secs

EVAL 0.204213947057724	0.03529667854309082 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n27
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TOTAL LOSS 0.1973131150007248
TRAIN 0.31024083495140076	0.8567779064178467 secs

EVAL 0.1973131150007248	0.035152435302734375 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n28
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TOTAL LOSS 0.19081085920333862
TRAIN 0.315265953540802	0.8657593727111816 secs

EVAL 0.19081085920333862	0.035311222076416016 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n29
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TRAIN 0.3093230724334717	0.8902115821838379 secs

EVAL 0.18467897176742554	0.03583073616027832 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n30
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TOTAL LOSS 0.17889267206192017
TRAIN 0.29337015748023987	0.8814165592193604 secs

EVAL 0.17889267206192017	0.03493332862854004 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n31
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TOTAL LOSS 0.17345263063907623
TRAIN 0.2936576306819916	0.8840427398681641 secs

EVAL 0.17345263063907623	0.03425788879394531 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n32
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TOTAL LOSS 0.16830497980117798
TRAIN 0.2913215160369873	0.860177755355835 secs

EVAL 0.16830497980117798	0.03480195999145508 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n33
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TOTAL LOSS 0.16345371305942535
TRAIN 0.2826239764690399	0.8526873588562012 secs

EVAL 0.16345371305942535	0.034911394119262695 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n34
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TOTAL LOSS 0.1589016616344452
TRAIN 0.2845451831817627	0.8792095184326172 secs

EVAL 0.1589016616344452	0.03438925743103027 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n35
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TRAIN 0.27568814158439636	0.8617627620697021 secs

EVAL 0.15462341904640198	0.033921241760253906 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n36
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TOTAL LOSS 0.15059134364128113
TRAIN 0.2753540575504303	0.8431141376495361 secs

EVAL 0.15059134364128113	0.033875226974487305 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n37
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TOTAL LOSS 0.14675086736679077
TRAIN 0.2767442464828491	0.8563065528869629 secs

EVAL 0.14675086736679077	0.034609317779541016 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n38
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TOTAL LOSS 0.14312049746513367
TRAIN 0.2711482644081116	0.8697116374969482 secs

EVAL 0.14312049746513367	0.03528642654418945 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n39
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TOTAL LOSS 0.1396811604499817
TRAIN 0.25966745615005493	0.8490116596221924 secs

EVAL 0.1396811604499817	0.03489494323730469 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n40
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TOTAL LOSS 0.13643859326839447
TRAIN 0.26513588428497314	0.8590366840362549 secs

EVAL 0.13643859326839447	0.03518271446228027 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n41
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TOTAL LOSS 0.13337942957878113
TRAIN 0.25799837708473206	1.1272213459014893 secs

EVAL 0.13337942957878113	0.03389739990234375 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n42
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TOTAL LOSS 0.13049913942813873
TRAIN 0.2629593312740326	0.8698751926422119 secs

EVAL 0.13049913942813873	0.03498339653015137 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n43
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TOTAL LOSS 0.1277955174446106
TRAIN 0.27107706665992737	0.8700039386749268 secs

EVAL 0.1277955174446106	0.03467893600463867 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n44
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TOTAL LOSS 0.1252259761095047
TRAIN 0.256503701210022	0.8785886764526367 secs

EVAL 0.1252259761095047	0.035221099853515625 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n45
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TOTAL LOSS 0.12278877943754196
TRAIN 0.25776246190071106	0.8933789730072021 secs

EVAL 0.12278877943754196	0.03492903709411621 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n46
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TRAIN 0.25358039140701294	0.9263026714324951 secs

EVAL 0.12048028409481049	0.03508877754211426 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n47
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TOTAL LOSS 0.1182904839515686
TRAIN 0.2463129758834839	0.9100334644317627 secs

EVAL 0.1182904839515686	0.03492093086242676 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n48
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TOTAL LOSS 0.11624590307474136
TRAIN 0.24976059794425964	0.8917226791381836 secs

EVAL 0.11624590307474136	0.035524845123291016 secs

** ** * Saving fine - tuned model ** ** * 
Epoch n49
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TOTAL LOSS 0.11430752277374268
TRAIN 0.25603052973747253	0.8890771865844727 secs

EVAL 0.11430752277374268	0.03390097618103027 secs

** ** * Saving fine - tuned model ** ** * 
Loading succesfull
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TOTAL LOSS 0.11978761106729507
tensor([[0.1061],
        [0.1163],
        [0.1183],
        [0.1199],
        [0.1079],
        [0.1088]])
tensor(0.)
tensor(nan)
tensor(0.)
tensor(0.)

EVALUATION AS STANDALONE MODULE¶

Below cell shows an exaple of how evaluation module can be used as a standalone module.

evaluation.Loss class can be instantiated and model output and ground truth can be passed to it to obtain results.

In the example below we captured model output and ground truth in a file and used that file to read the data.

In function definition loss(prob,truth,logits,False)

prob -> List of Output predicted probabilities of case being positive

truth -> List of ground truth labels

logits -> List of logits obtained from last fully connected layer before applying softmax.sigmoid function in the model.

In [ ]:
if torch.cuda.is_available():
    device='cuda:0'
#device='cpu'
loss=evaluation.Loss(device,acc=True,ppv=True,sensi=True,tnr=True,npv=True,auroc=True,aurocPlot=True,auprc=True,auprcPlot=True,callb=True,callbPlot=True)
with open("./data/output/outputDict", 'rb') as fp:
    outputDict=pickle.load(fp)
prob=list(outputDict['Prob'])
truth=list(outputDict['Labels'])
logits=list(outputDict['Logits'])
#print(torch.tensor(prob))
print("======= TESTING ========")
loss(prob,truth,logits,train=False,standalone=True)

======= TESTING ========
BCE Loss: 0.98
AU-ROC: 0.87
AU-PRC: 0.86
AU-PRC Baaseline: 0.49
Accuracy: 0.80
Precision: 0.78
Recall: 0.82
Specificity: 0.77
NPV: 0.82
ECE: 0.06
MCE: 0.14

11. FAIRNESS EVALUATION¶

In train and testing step we save output files in ./data/output/ folder.

This file conatins list of demographic variables included in training and testing of the model.

It also contains the ground truth labels and predicted probability for each sample.

We use the above saved data to perform fairness evaluation of the results obtained from model testing.

This module can be used as stand-alone module also.

Please create a file that contains predicted probabilites form the last sigmoid layer in column named Prob and ground truth labels for each sample in column named Labels.

In [ ]:
fairness.fairness_evaluation(inputFile='outputDict',outputFile='fairnessReport')
sensitive_attribute group tp tn fp fn tpr tnr fpr fnr pr nr accuracy
0 ethnicity AMERICAN INDIAN/ALASKA NATIVE 3 5 0 1 0.750000 1.000000 0.000000 0.250000 0.333333 0.666667 0.888889
1 ethnicity ASIAN 0 6 2 4 0.000000 0.750000 0.250000 1.000000 0.166667 0.833333 0.500000
2 ethnicity ASIAN - ASIAN INDIAN 4 0 1 0 1.000000 0.000000 1.000000 0.000000 1.000000 0.000000 0.800000
3 ethnicity ASIAN - CHINESE 8 6 2 4 0.666667 0.750000 0.250000 0.333333 0.500000 0.500000 0.700000
4 ethnicity ASIAN - SOUTH EAST ASIAN 2 5 1 0 1.000000 0.833333 0.166667 0.000000 0.375000 0.625000 0.875000
5 ethnicity BLACK/AFRICAN 0 8 0 0 nan 1.000000 0.000000 nan 0.000000 1.000000 1.000000
6 ethnicity BLACK/AFRICAN AMERICAN 88 107 22 24 0.785714 0.829457 0.170543 0.214286 0.456432 0.543568 0.809129
7 ethnicity BLACK/CAPE VERDEAN 0 4 1 0 nan 0.800000 0.200000 nan 0.200000 0.800000 0.800000
8 ethnicity BLACK/CARIBBEAN ISLAND 1 4 1 1 0.500000 0.800000 0.200000 0.500000 0.285714 0.714286 0.714286
9 ethnicity HISPANIC OR LATINO 2 3 1 0 1.000000 0.750000 0.250000 0.000000 0.500000 0.500000 0.833333
10 ethnicity HISPANIC/LATINO - CENTRAL AMERICAN 0 1 0 0 nan 1.000000 0.000000 nan 0.000000 1.000000 1.000000
11 ethnicity HISPANIC/LATINO - COLUMBIAN 0 1 0 0 nan 1.000000 0.000000 nan 0.000000 1.000000 1.000000
12 ethnicity HISPANIC/LATINO - CUBAN 3 1 0 0 1.000000 1.000000 0.000000 0.000000 0.750000 0.250000 1.000000
13 ethnicity HISPANIC/LATINO - DOMINICAN 2 6 0 0 1.000000 1.000000 0.000000 0.000000 0.250000 0.750000 1.000000
14 ethnicity HISPANIC/LATINO - GUATEMALAN 3 0 1 0 1.000000 0.000000 1.000000 0.000000 1.000000 0.000000 0.750000
15 ethnicity HISPANIC/LATINO - MEXICAN 0 1 0 0 nan 1.000000 0.000000 nan 0.000000 1.000000 1.000000
16 ethnicity HISPANIC/LATINO - PUERTO RICAN 20 12 4 2 0.909091 0.750000 0.250000 0.090909 0.631579 0.368421 0.842105
17 ethnicity HISPANIC/LATINO - SALVADORAN 3 1 0 0 1.000000 1.000000 0.000000 0.000000 0.750000 0.250000 1.000000
18 ethnicity MULTIPLE RACE/ETHNICITY 1 0 0 0 1.000000 nan nan 0.000000 1.000000 0.000000 1.000000
19 ethnicity NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER 0 1 0 0 nan 1.000000 0.000000 nan 0.000000 1.000000 1.000000
20 ethnicity OTHER 15 19 4 6 0.714286 0.826087 0.173913 0.285714 0.431818 0.568182 0.772727
21 ethnicity PATIENT DECLINED TO ANSWER 0 1 1 0 nan 0.500000 0.500000 nan 0.500000 0.500000 0.500000
22 ethnicity PORTUGUESE 7 2 3 0 1.000000 0.400000 0.600000 0.000000 0.833333 0.166667 0.750000
23 ethnicity UNABLE TO OBTAIN 9 2 1 1 0.900000 0.666667 0.333333 0.100000 0.769231 0.230769 0.846154
24 ethnicity UNKNOWN 111 48 7 15 0.880952 0.872727 0.127273 0.119048 0.651934 0.348066 0.878453
25 ethnicity WHITE 416 447 151 92 0.818898 0.747492 0.252508 0.181102 0.512658 0.487342 0.780289
26 ethnicity WHITE - EASTERN EUROPEAN 1 0 0 0 1.000000 nan nan 0.000000 1.000000 0.000000 1.000000
27 ethnicity WHITE - OTHER EUROPEAN 16 13 2 8 0.666667 0.866667 0.133333 0.333333 0.461538 0.538462 0.743590
28 ethnicity WHITE - RUSSIAN 7 7 3 1 0.875000 0.700000 0.300000 0.125000 0.555556 0.444444 0.777778
29 gender F 278 265 99 65 0.810496 0.728022 0.271978 0.189504 0.533239 0.466761 0.768034
30 gender M 444 446 109 94 0.825279 0.803604 0.196396 0.174721 0.505947 0.494053 0.814273
31 age_binned 20-30 0 12 0 1 0.000000 1.000000 0.000000 1.000000 0.000000 1.000000 0.923077
32 age_binned 30-40 11 10 4 3 0.785714 0.714286 0.285714 0.214286 0.535714 0.464286 0.750000
33 age_binned 40-50 33 50 8 15 0.687500 0.862069 0.137931 0.312500 0.386792 0.613208 0.783019
34 age_binned 50-60 84 116 18 25 0.770642 0.865672 0.134328 0.229358 0.419753 0.580247 0.823045
35 age_binned 60-70 156 167 50 39 0.800000 0.769585 0.230415 0.200000 0.500000 0.500000 0.783981
36 age_binned 70-80 206 181 68 39 0.840816 0.726908 0.273092 0.159184 0.554656 0.445344 0.783401
37 age_binned 80-90 201 140 46 33 0.858974 0.752688 0.247312 0.141026 0.588095 0.411905 0.811905
38 age_binned 90-100 31 35 14 4 0.885714 0.714286 0.285714 0.114286 0.535714 0.464286 0.785714

12. MODEL CALLIBRATION¶

Please run below cell if you want to callibrate predicted probabilites of the model on test data. It will use the output saved during the testing of the model.

The file is saved in ./data/output/.

This module can be used as stand-alone module also.

Please create a file that contain predicted logits form the last fully connected layer in column named Logits and
ground truth labels for each sample in a column named Labels.

In [ ]:
callibrate_output.callibrate(inputFile='outputDict',outputFile='callibratedResults')

BEFORE CALLIBRATION
BCE Loss: 0.98
AU-ROC: 0.87
AU-PRC: 0.86
AU-PRC Baaseline: 0.49
Accuracy: 0.80
Precision: 0.78
Recall: 0.82
Specificity: 0.77
NPV: 0.82
ECE: 0.06
MCE: 0.14
AFTER CALLIBRATION
BCE Loss: 0.94
AU-ROC: 0.87
AU-PRC: 0.86
AU-PRC Baaseline: 0.49
Accuracy: 0.80
Precision: 0.78
Recall: 0.82
Specificity: 0.77
NPV: 0.82
ECE: 0.04
MCE: 0.08
In [ ]: